R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(44164
+ ,-9
+ ,-7.7
+ ,544686
+ ,2.2
+ ,40399
+ ,-13
+ ,-4.9
+ ,537034
+ ,2.2
+ ,36763
+ ,-8
+ ,-2.4
+ ,551531
+ ,2.2
+ ,37903
+ ,-13
+ ,-3.6
+ ,563250
+ ,1.6
+ ,35532
+ ,-15
+ ,-7
+ ,574761
+ ,1.6
+ ,35533
+ ,-15
+ ,-7
+ ,580112
+ ,1.6
+ ,32110
+ ,-15
+ ,-7.9
+ ,575093
+ ,-0.1
+ ,33374
+ ,-10
+ ,-8.8
+ ,557560
+ ,-0.1
+ ,35462
+ ,-12
+ ,-14.2
+ ,564478
+ ,-0.1
+ ,33508
+ ,-11
+ ,-17.8
+ ,580523
+ ,-2.7
+ ,36080
+ ,-11
+ ,-18.2
+ ,596594
+ ,-2.7
+ ,34560
+ ,-17
+ ,-22.8
+ ,586570
+ ,-2.7
+ ,38737
+ ,-18
+ ,-23.6
+ ,536214
+ ,-4.1
+ ,38144
+ ,-19
+ ,-27.6
+ ,523597
+ ,-4.1
+ ,37594
+ ,-22
+ ,-29.4
+ ,536535
+ ,-4.1
+ ,36424
+ ,-24
+ ,-31.8
+ ,536322
+ ,-3.7
+ ,36843
+ ,-24
+ ,-31.4
+ ,532638
+ ,-3.7
+ ,37246
+ ,-20
+ ,-27.6
+ ,528222
+ ,-3.7
+ ,38661
+ ,-25
+ ,-28.8
+ ,516141
+ ,-1.3
+ ,40454
+ ,-22
+ ,-21.9
+ ,501866
+ ,-1.3
+ ,44928
+ ,-17
+ ,-13.9
+ ,506174
+ ,-1.3
+ ,48441
+ ,-9
+ ,-8
+ ,517945
+ ,1.1
+ ,48140
+ ,-11
+ ,-2.8
+ ,533590
+ ,1.1
+ ,45998
+ ,-13
+ ,-3.3
+ ,528379
+ ,1.1
+ ,47369
+ ,-11
+ ,-1.3
+ ,477580
+ ,1.9
+ ,49554
+ ,-9
+ ,0.5
+ ,469357
+ ,1.9
+ ,47510
+ ,-7
+ ,-1.9
+ ,490243
+ ,1.9
+ ,44873
+ ,-3
+ ,2
+ ,492622
+ ,1.6
+ ,45344
+ ,-3
+ ,1.7
+ ,507561
+ ,1.6
+ ,42413
+ ,-6
+ ,1.9
+ ,516922
+ ,1.6
+ ,36912
+ ,-4
+ ,0.1
+ ,514258
+ ,1.8
+ ,43452
+ ,-8
+ ,2.4
+ ,509846
+ ,1.8
+ ,42142
+ ,-1
+ ,2.3
+ ,527070
+ ,1.8
+ ,44382
+ ,-2
+ ,4.7
+ ,541657
+ ,2.7
+ ,43636
+ ,-2
+ ,5
+ ,564591
+ ,2.7
+ ,44167
+ ,-1
+ ,7.2
+ ,555362
+ ,2.7
+ ,44423
+ ,1
+ ,8.5
+ ,498662
+ ,3.3
+ ,42868
+ ,2
+ ,6.8
+ ,511038
+ ,3.3
+ ,43908
+ ,2
+ ,5.8
+ ,525919
+ ,3.3
+ ,42013
+ ,-1
+ ,3.7
+ ,531673
+ ,3.4
+ ,38846
+ ,1
+ ,4.8
+ ,548854
+ ,3.4
+ ,35087
+ ,-1
+ ,6.1
+ ,560576
+ ,3.4
+ ,33026
+ ,-8
+ ,6.9
+ ,557274
+ ,3
+ ,34646
+ ,1
+ ,5.7
+ ,565742
+ ,3
+ ,37135
+ ,2
+ ,6.9
+ ,587625
+ ,3
+ ,37985
+ ,-2
+ ,5.5
+ ,619916
+ ,2.6
+ ,43121
+ ,-2
+ ,6.5
+ ,625809
+ ,2.6
+ ,43722
+ ,-2
+ ,7.7
+ ,619567
+ ,2.6
+ ,43630
+ ,-2
+ ,6.3
+ ,572942
+ ,2.4
+ ,42234
+ ,-6
+ ,5.5
+ ,572775
+ ,2.4
+ ,39351
+ ,-4
+ ,5.3
+ ,574205
+ ,2.4
+ ,39327
+ ,-5
+ ,3.3
+ ,579799
+ ,2.8
+ ,35704
+ ,-2
+ ,2.2
+ ,590072
+ ,2.8
+ ,30466
+ ,-1
+ ,0.6
+ ,593408
+ ,2.8
+ ,28155
+ ,-5
+ ,0.2
+ ,597141
+ ,2.3
+ ,29257
+ ,-9
+ ,-0.7
+ ,595404
+ ,2.3
+ ,29998
+ ,-8
+ ,-1.7
+ ,612117
+ ,2.3
+ ,32529
+ ,-14
+ ,-3.7
+ ,628232
+ ,1.8
+ ,34787
+ ,-10
+ ,-7.6
+ ,628884
+ ,1.8
+ ,33855
+ ,-11
+ ,-8.2
+ ,620735
+ ,1.8
+ ,34556
+ ,-11
+ ,-7.5
+ ,569028
+ ,2
+ ,31348
+ ,-11
+ ,-8
+ ,567456
+ ,2
+ ,30805
+ ,-5
+ ,-6.9
+ ,573100
+ ,2
+ ,28353
+ ,-2
+ ,-4.2
+ ,584428
+ ,1.9
+ ,24514
+ ,-3
+ ,-3.6
+ ,589379
+ ,1.9
+ ,21106
+ ,-6
+ ,-1.8
+ ,590865
+ ,1.9
+ ,21346
+ ,-6
+ ,-3.2
+ ,595454
+ ,3.1
+ ,23335
+ ,-7
+ ,-1.3
+ ,594167
+ ,3.1
+ ,24379
+ ,-6
+ ,0.6
+ ,611324
+ ,3.1
+ ,26290
+ ,-2
+ ,1.2
+ ,612613
+ ,3.6
+ ,30084
+ ,-2
+ ,0.4
+ ,610763
+ ,3.6
+ ,29429
+ ,-4
+ ,3
+ ,593530
+ ,3.6
+ ,30632
+ ,0
+ ,-0.4
+ ,542722
+ ,3
+ ,27349
+ ,-6
+ ,0
+ ,536662
+ ,3
+ ,27264
+ ,-4
+ ,-1.3
+ ,543599
+ ,3
+ ,27474
+ ,-3
+ ,-3.1
+ ,555332
+ ,2.5
+ ,24482
+ ,-1
+ ,-4
+ ,560854
+ ,2.5
+ ,21453
+ ,-3
+ ,-4.9
+ ,562325
+ ,2.5
+ ,18788
+ ,-6
+ ,-4.6
+ ,554788
+ ,1
+ ,19282
+ ,-6
+ ,-5.4
+ ,547344
+ ,1
+ ,19713
+ ,-15
+ ,-8.1
+ ,565464
+ ,1
+ ,21917
+ ,-5
+ ,-9.4
+ ,577992
+ ,0.5
+ ,23812
+ ,-11
+ ,-12.6
+ ,579714
+ ,0.5
+ ,23785
+ ,-13
+ ,-15.7
+ ,569323
+ ,0.5
+ ,24696
+ ,-10
+ ,-17.3
+ ,506971
+ ,0.6
+ ,24562
+ ,-9
+ ,-14.4
+ ,500857
+ ,0.6
+ ,23580
+ ,-11
+ ,-16.2
+ ,509127
+ ,0.6
+ ,24939
+ ,-18
+ ,-14.9
+ ,509933
+ ,1
+ ,23899
+ ,-13
+ ,-11
+ ,517009
+ ,1
+ ,21454
+ ,-9
+ ,-11.5
+ ,519164
+ ,1
+ ,19761
+ ,-8
+ ,-9.6
+ ,512238
+ ,2.1
+ ,19815
+ ,-4
+ ,-8.8
+ ,509239
+ ,2.1
+ ,20780
+ ,-3
+ ,-9.7
+ ,518585
+ ,2.1
+ ,23462
+ ,-3
+ ,-8.4
+ ,522975
+ ,1.8
+ ,25005
+ ,-3
+ ,-8.4
+ ,525192
+ ,1.8
+ ,24725
+ ,-1
+ ,-6.8
+ ,516847
+ ,1.8
+ ,26198
+ ,0
+ ,-5.3
+ ,455626
+ ,0.9
+ ,27543
+ ,1
+ ,-5.1
+ ,454724
+ ,0.9
+ ,26471
+ ,0
+ ,-6.5
+ ,461251
+ ,0.9
+ ,26558
+ ,2
+ ,-7.3
+ ,470439
+ ,0.6
+ ,25317
+ ,1
+ ,-10.8
+ ,474605
+ ,0.6
+ ,22896
+ ,-1
+ ,-10.9
+ ,476049
+ ,0.6)
+ ,dim=c(5
+ ,102)
+ ,dimnames=list(c('Vacatures'
+ ,'Consumentenvertrouwen'
+ ,'producentenvertrouwen'
+ ,'nietwerkendewerkzoekende'
+ ,'economischegroei')
+ ,1:102))
> y <- array(NA,dim=c(5,102),dimnames=list(c('Vacatures','Consumentenvertrouwen','producentenvertrouwen','nietwerkendewerkzoekende','economischegroei'),1:102))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Vacatures Consumentenvertrouwen producentenvertrouwen
1 44164 -9 -7.7
2 40399 -13 -4.9
3 36763 -8 -2.4
4 37903 -13 -3.6
5 35532 -15 -7.0
6 35533 -15 -7.0
7 32110 -15 -7.9
8 33374 -10 -8.8
9 35462 -12 -14.2
10 33508 -11 -17.8
11 36080 -11 -18.2
12 34560 -17 -22.8
13 38737 -18 -23.6
14 38144 -19 -27.6
15 37594 -22 -29.4
16 36424 -24 -31.8
17 36843 -24 -31.4
18 37246 -20 -27.6
19 38661 -25 -28.8
20 40454 -22 -21.9
21 44928 -17 -13.9
22 48441 -9 -8.0
23 48140 -11 -2.8
24 45998 -13 -3.3
25 47369 -11 -1.3
26 49554 -9 0.5
27 47510 -7 -1.9
28 44873 -3 2.0
29 45344 -3 1.7
30 42413 -6 1.9
31 36912 -4 0.1
32 43452 -8 2.4
33 42142 -1 2.3
34 44382 -2 4.7
35 43636 -2 5.0
36 44167 -1 7.2
37 44423 1 8.5
38 42868 2 6.8
39 43908 2 5.8
40 42013 -1 3.7
41 38846 1 4.8
42 35087 -1 6.1
43 33026 -8 6.9
44 34646 1 5.7
45 37135 2 6.9
46 37985 -2 5.5
47 43121 -2 6.5
48 43722 -2 7.7
49 43630 -2 6.3
50 42234 -6 5.5
51 39351 -4 5.3
52 39327 -5 3.3
53 35704 -2 2.2
54 30466 -1 0.6
55 28155 -5 0.2
56 29257 -9 -0.7
57 29998 -8 -1.7
58 32529 -14 -3.7
59 34787 -10 -7.6
60 33855 -11 -8.2
61 34556 -11 -7.5
62 31348 -11 -8.0
63 30805 -5 -6.9
64 28353 -2 -4.2
65 24514 -3 -3.6
66 21106 -6 -1.8
67 21346 -6 -3.2
68 23335 -7 -1.3
69 24379 -6 0.6
70 26290 -2 1.2
71 30084 -2 0.4
72 29429 -4 3.0
73 30632 0 -0.4
74 27349 -6 0.0
75 27264 -4 -1.3
76 27474 -3 -3.1
77 24482 -1 -4.0
78 21453 -3 -4.9
79 18788 -6 -4.6
80 19282 -6 -5.4
81 19713 -15 -8.1
82 21917 -5 -9.4
83 23812 -11 -12.6
84 23785 -13 -15.7
85 24696 -10 -17.3
86 24562 -9 -14.4
87 23580 -11 -16.2
88 24939 -18 -14.9
89 23899 -13 -11.0
90 21454 -9 -11.5
91 19761 -8 -9.6
92 19815 -4 -8.8
93 20780 -3 -9.7
94 23462 -3 -8.4
95 25005 -3 -8.4
96 24725 -1 -6.8
97 26198 0 -5.3
98 27543 1 -5.1
99 26471 0 -6.5
100 26558 2 -7.3
101 25317 1 -10.8
102 22896 -1 -10.9
nietwerkendewerkzoekende economischegroei
1 544686 2.2
2 537034 2.2
3 551531 2.2
4 563250 1.6
5 574761 1.6
6 580112 1.6
7 575093 -0.1
8 557560 -0.1
9 564478 -0.1
10 580523 -2.7
11 596594 -2.7
12 586570 -2.7
13 536214 -4.1
14 523597 -4.1
15 536535 -4.1
16 536322 -3.7
17 532638 -3.7
18 528222 -3.7
19 516141 -1.3
20 501866 -1.3
21 506174 -1.3
22 517945 1.1
23 533590 1.1
24 528379 1.1
25 477580 1.9
26 469357 1.9
27 490243 1.9
28 492622 1.6
29 507561 1.6
30 516922 1.6
31 514258 1.8
32 509846 1.8
33 527070 1.8
34 541657 2.7
35 564591 2.7
36 555362 2.7
37 498662 3.3
38 511038 3.3
39 525919 3.3
40 531673 3.4
41 548854 3.4
42 560576 3.4
43 557274 3.0
44 565742 3.0
45 587625 3.0
46 619916 2.6
47 625809 2.6
48 619567 2.6
49 572942 2.4
50 572775 2.4
51 574205 2.4
52 579799 2.8
53 590072 2.8
54 593408 2.8
55 597141 2.3
56 595404 2.3
57 612117 2.3
58 628232 1.8
59 628884 1.8
60 620735 1.8
61 569028 2.0
62 567456 2.0
63 573100 2.0
64 584428 1.9
65 589379 1.9
66 590865 1.9
67 595454 3.1
68 594167 3.1
69 611324 3.1
70 612613 3.6
71 610763 3.6
72 593530 3.6
73 542722 3.0
74 536662 3.0
75 543599 3.0
76 555332 2.5
77 560854 2.5
78 562325 2.5
79 554788 1.0
80 547344 1.0
81 565464 1.0
82 577992 0.5
83 579714 0.5
84 569323 0.5
85 506971 0.6
86 500857 0.6
87 509127 0.6
88 509933 1.0
89 517009 1.0
90 519164 1.0
91 512238 2.1
92 509239 2.1
93 518585 2.1
94 522975 1.8
95 525192 1.8
96 516847 1.8
97 455626 0.9
98 454724 0.9
99 461251 0.9
100 470439 0.6
101 474605 0.6
102 476049 0.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Consumentenvertrouwen producentenvertrouwen
69102.1320 -926.0246 1529.4144
nietwerkendewerkzoekende economischegroei
-0.0511 -4442.6163
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17555.3 -3251.4 455.3 3713.5 16109.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.910e+04 8.293e+03 8.332 5.25e-13 ***
Consumentenvertrouwen -9.260e+02 1.539e+02 -6.018 3.14e-08 ***
producentenvertrouwen 1.529e+03 1.523e+02 10.042 < 2e-16 ***
nietwerkendewerkzoekende -5.110e-02 1.528e-02 -3.344 0.00117 **
economischegroei -4.443e+03 7.118e+02 -6.241 1.14e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5990 on 97 degrees of freedom
Multiple R-squared: 0.5222, Adjusted R-squared: 0.5025
F-statistic: 26.5 on 4 and 97 DF, p-value: 7.307e-15
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.705657e-02 1.141131e-01 9.429434e-01
[2,] 1.884384e-02 3.768768e-02 9.811562e-01
[3,] 1.808318e-02 3.616635e-02 9.819168e-01
[4,] 2.071520e-02 4.143039e-02 9.792848e-01
[5,] 7.865697e-03 1.573139e-02 9.921343e-01
[6,] 4.551648e-03 9.103296e-03 9.954484e-01
[7,] 2.300677e-03 4.601353e-03 9.976993e-01
[8,] 8.771393e-04 1.754279e-03 9.991229e-01
[9,] 5.375014e-04 1.075003e-03 9.994625e-01
[10,] 2.312734e-04 4.625469e-04 9.997687e-01
[11,] 9.002287e-05 1.800457e-04 9.999100e-01
[12,] 7.204316e-05 1.440863e-04 9.999280e-01
[13,] 3.403492e-05 6.806984e-05 9.999660e-01
[14,] 1.060691e-04 2.121381e-04 9.998939e-01
[15,] 5.226748e-04 1.045350e-03 9.994773e-01
[16,] 2.736059e-03 5.472117e-03 9.972639e-01
[17,] 2.494857e-03 4.989713e-03 9.975051e-01
[18,] 2.378783e-03 4.757566e-03 9.976212e-01
[19,] 1.779436e-03 3.558872e-03 9.982206e-01
[20,] 1.755204e-03 3.510408e-03 9.982448e-01
[21,] 2.052924e-03 4.105848e-03 9.979471e-01
[22,] 1.468525e-03 2.937049e-03 9.985315e-01
[23,] 1.034296e-03 2.068592e-03 9.989657e-01
[24,] 4.565029e-03 9.130059e-03 9.954350e-01
[25,] 3.016426e-03 6.032851e-03 9.969836e-01
[26,] 2.089591e-03 4.179182e-03 9.979104e-01
[27,] 1.969494e-03 3.938988e-03 9.980305e-01
[28,] 2.359134e-03 4.718268e-03 9.976409e-01
[29,] 2.034683e-03 4.069367e-03 9.979653e-01
[30,] 1.811846e-03 3.623693e-03 9.981882e-01
[31,] 1.697560e-03 3.395119e-03 9.983024e-01
[32,] 1.761699e-03 3.523397e-03 9.982383e-01
[33,] 2.160959e-03 4.321919e-03 9.978390e-01
[34,] 2.121980e-03 4.243960e-03 9.978780e-01
[35,] 2.624662e-03 5.249325e-03 9.973753e-01
[36,] 6.561354e-03 1.312271e-02 9.934386e-01
[37,] 6.084824e-03 1.216965e-02 9.939152e-01
[38,] 4.050466e-03 8.100931e-03 9.959495e-01
[39,] 4.612249e-03 9.224499e-03 9.953878e-01
[40,] 1.931445e-02 3.862889e-02 9.806856e-01
[41,] 4.057704e-02 8.115408e-02 9.594230e-01
[42,] 5.784877e-02 1.156975e-01 9.421512e-01
[43,] 7.442827e-02 1.488565e-01 9.255717e-01
[44,] 8.903845e-02 1.780769e-01 9.109615e-01
[45,] 1.440143e-01 2.880286e-01 8.559857e-01
[46,] 1.786726e-01 3.573451e-01 8.213274e-01
[47,] 2.017042e-01 4.034085e-01 7.982958e-01
[48,] 2.602500e-01 5.205000e-01 7.397500e-01
[49,] 2.916039e-01 5.832078e-01 7.083961e-01
[50,] 2.734190e-01 5.468379e-01 7.265810e-01
[51,] 2.814705e-01 5.629411e-01 7.185295e-01
[52,] 4.198323e-01 8.396646e-01 5.801677e-01
[53,] 5.898548e-01 8.202905e-01 4.101452e-01
[54,] 8.136728e-01 3.726543e-01 1.863272e-01
[55,] 9.135358e-01 1.729283e-01 8.646417e-02
[56,] 9.586321e-01 8.273584e-02 4.136792e-02
[57,] 9.722397e-01 5.552067e-02 2.776034e-02
[58,] 9.795844e-01 4.083115e-02 2.041558e-02
[59,] 9.939360e-01 1.212792e-02 6.063960e-03
[60,] 9.966327e-01 6.734662e-03 3.367331e-03
[61,] 9.970612e-01 5.877685e-03 2.938843e-03
[62,] 9.964013e-01 7.197474e-03 3.598737e-03
[63,] 9.941930e-01 1.161403e-02 5.807014e-03
[64,] 9.950592e-01 9.881589e-03 4.940794e-03
[65,] 9.957421e-01 8.515786e-03 4.257893e-03
[66,] 9.983550e-01 3.290012e-03 1.645006e-03
[67,] 9.990356e-01 1.928852e-03 9.644259e-04
[68,] 9.995742e-01 8.516358e-04 4.258179e-04
[69,] 9.999478e-01 1.043668e-04 5.218338e-05
[70,] 9.999780e-01 4.396290e-05 2.198145e-05
[71,] 9.999746e-01 5.080688e-05 2.540344e-05
[72,] 9.999923e-01 1.536302e-05 7.681508e-06
[73,] 9.999980e-01 3.958495e-06 1.979247e-06
[74,] 9.999994e-01 1.256227e-06 6.281135e-07
[75,] 9.999994e-01 1.274943e-06 6.374713e-07
[76,] 9.999982e-01 3.685344e-06 1.842672e-06
[77,] 9.999941e-01 1.181962e-05 5.909812e-06
[78,] 9.999891e-01 2.183521e-05 1.091761e-05
[79,] 9.999674e-01 6.513702e-05 3.256851e-05
[80,] 9.999059e-01 1.881579e-04 9.407893e-05
[81,] 9.999965e-01 7.082340e-06 3.541170e-06
[82,] 9.999961e-01 7.711006e-06 3.855503e-06
[83,] 9.999820e-01 3.601199e-05 1.800599e-05
[84,] 9.999019e-01 1.962720e-04 9.813601e-05
[85,] 9.996264e-01 7.472954e-04 3.736477e-04
[86,] 9.989371e-01 2.125722e-03 1.062861e-03
[87,] 9.933049e-01 1.339023e-02 6.695116e-03
> postscript(file="/var/www/html/rcomp/tmp/1iaxq1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2iaxq1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3iaxq1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bkxt1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bkxt1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 102
Frequency = 1
1 2 3 4 5
16109.527797 3967.077488 1878.412702 -1843.181046 -278.047646
6 7 8 9 10
-3.629429 -9859.058372 -3484.340160 5363.935088 -889.105089
11 12 13 14 15
3115.835061 2562.800843 -1755.378415 2198.568052 2284.528755
16 17 18 19 20
4699.237074 4318.231227 2387.912039 11053.066845 4341.776479
21 22 23 24 25
1430.708655 14592.098413 5285.501573 1789.894904 2912.549231
26 27 28 29 30
3776.484679 8322.333218 2213.489479 3906.646773 -1629.994067
31 32 33 34 35
-1773.597195 -2680.787134 3524.415286 5911.497794 5878.524229
36 37 38 39 40
3499.266088 3387.465848 5990.867156 9320.650922 8597.619187
41 42 43 44 45
6478.204214 -522.128662 -12234.600024 -12.394920 2685.480561
46 47 48 49 50
1845.478000 5753.176207 4199.933590 2978.208801 -906.891190
51 52 53 54 55
-1558.890906 2612.794539 3975.140120 2280.686201 -5153.210752
56 57 58 59 60
-6467.591056 -2417.173620 -3781.377900 8178.751641 6821.989022
61 62 63 64 65
4698.867316 2175.250568 5794.431819 2125.648027 -3304.045650
66 67 68 69 70
-12167.135708 -4220.333381 -6129.006728 -6188.204132 796.417358
71 72 73 74 75
5719.420059 -1644.655268 3200.763251 -6559.795807 -2450.050389
76 77 78 79 80
-182.870961 335.806926 -3093.606115 -16044.544229 -14707.376232
81 82 83 84 85
-17555.307240 -5683.991842 -4363.024873 -2031.834796 1362.584831
86 87 88 89 90
-2593.097300 -2251.631204 -7544.811623 -9557.844865 -7423.926001
91 92 93 94 95
-6563.806247 -4182.478276 -437.431360 -852.140650 804.140640
96 97 98 99 100
-497.274824 -7518.915034 -5599.862508 -5123.199055 -2823.927161
101 102
574.867319 -3471.456851
> postscript(file="/var/www/html/rcomp/tmp/6bkxt1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 102
Frequency = 1
lag(myerror, k = 1) myerror
0 16109.527797 NA
1 3967.077488 16109.527797
2 1878.412702 3967.077488
3 -1843.181046 1878.412702
4 -278.047646 -1843.181046
5 -3.629429 -278.047646
6 -9859.058372 -3.629429
7 -3484.340160 -9859.058372
8 5363.935088 -3484.340160
9 -889.105089 5363.935088
10 3115.835061 -889.105089
11 2562.800843 3115.835061
12 -1755.378415 2562.800843
13 2198.568052 -1755.378415
14 2284.528755 2198.568052
15 4699.237074 2284.528755
16 4318.231227 4699.237074
17 2387.912039 4318.231227
18 11053.066845 2387.912039
19 4341.776479 11053.066845
20 1430.708655 4341.776479
21 14592.098413 1430.708655
22 5285.501573 14592.098413
23 1789.894904 5285.501573
24 2912.549231 1789.894904
25 3776.484679 2912.549231
26 8322.333218 3776.484679
27 2213.489479 8322.333218
28 3906.646773 2213.489479
29 -1629.994067 3906.646773
30 -1773.597195 -1629.994067
31 -2680.787134 -1773.597195
32 3524.415286 -2680.787134
33 5911.497794 3524.415286
34 5878.524229 5911.497794
35 3499.266088 5878.524229
36 3387.465848 3499.266088
37 5990.867156 3387.465848
38 9320.650922 5990.867156
39 8597.619187 9320.650922
40 6478.204214 8597.619187
41 -522.128662 6478.204214
42 -12234.600024 -522.128662
43 -12.394920 -12234.600024
44 2685.480561 -12.394920
45 1845.478000 2685.480561
46 5753.176207 1845.478000
47 4199.933590 5753.176207
48 2978.208801 4199.933590
49 -906.891190 2978.208801
50 -1558.890906 -906.891190
51 2612.794539 -1558.890906
52 3975.140120 2612.794539
53 2280.686201 3975.140120
54 -5153.210752 2280.686201
55 -6467.591056 -5153.210752
56 -2417.173620 -6467.591056
57 -3781.377900 -2417.173620
58 8178.751641 -3781.377900
59 6821.989022 8178.751641
60 4698.867316 6821.989022
61 2175.250568 4698.867316
62 5794.431819 2175.250568
63 2125.648027 5794.431819
64 -3304.045650 2125.648027
65 -12167.135708 -3304.045650
66 -4220.333381 -12167.135708
67 -6129.006728 -4220.333381
68 -6188.204132 -6129.006728
69 796.417358 -6188.204132
70 5719.420059 796.417358
71 -1644.655268 5719.420059
72 3200.763251 -1644.655268
73 -6559.795807 3200.763251
74 -2450.050389 -6559.795807
75 -182.870961 -2450.050389
76 335.806926 -182.870961
77 -3093.606115 335.806926
78 -16044.544229 -3093.606115
79 -14707.376232 -16044.544229
80 -17555.307240 -14707.376232
81 -5683.991842 -17555.307240
82 -4363.024873 -5683.991842
83 -2031.834796 -4363.024873
84 1362.584831 -2031.834796
85 -2593.097300 1362.584831
86 -2251.631204 -2593.097300
87 -7544.811623 -2251.631204
88 -9557.844865 -7544.811623
89 -7423.926001 -9557.844865
90 -6563.806247 -7423.926001
91 -4182.478276 -6563.806247
92 -437.431360 -4182.478276
93 -852.140650 -437.431360
94 804.140640 -852.140650
95 -497.274824 804.140640
96 -7518.915034 -497.274824
97 -5599.862508 -7518.915034
98 -5123.199055 -5599.862508
99 -2823.927161 -5123.199055
100 574.867319 -2823.927161
101 -3471.456851 574.867319
102 NA -3471.456851
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3967.077488 16109.527797
[2,] 1878.412702 3967.077488
[3,] -1843.181046 1878.412702
[4,] -278.047646 -1843.181046
[5,] -3.629429 -278.047646
[6,] -9859.058372 -3.629429
[7,] -3484.340160 -9859.058372
[8,] 5363.935088 -3484.340160
[9,] -889.105089 5363.935088
[10,] 3115.835061 -889.105089
[11,] 2562.800843 3115.835061
[12,] -1755.378415 2562.800843
[13,] 2198.568052 -1755.378415
[14,] 2284.528755 2198.568052
[15,] 4699.237074 2284.528755
[16,] 4318.231227 4699.237074
[17,] 2387.912039 4318.231227
[18,] 11053.066845 2387.912039
[19,] 4341.776479 11053.066845
[20,] 1430.708655 4341.776479
[21,] 14592.098413 1430.708655
[22,] 5285.501573 14592.098413
[23,] 1789.894904 5285.501573
[24,] 2912.549231 1789.894904
[25,] 3776.484679 2912.549231
[26,] 8322.333218 3776.484679
[27,] 2213.489479 8322.333218
[28,] 3906.646773 2213.489479
[29,] -1629.994067 3906.646773
[30,] -1773.597195 -1629.994067
[31,] -2680.787134 -1773.597195
[32,] 3524.415286 -2680.787134
[33,] 5911.497794 3524.415286
[34,] 5878.524229 5911.497794
[35,] 3499.266088 5878.524229
[36,] 3387.465848 3499.266088
[37,] 5990.867156 3387.465848
[38,] 9320.650922 5990.867156
[39,] 8597.619187 9320.650922
[40,] 6478.204214 8597.619187
[41,] -522.128662 6478.204214
[42,] -12234.600024 -522.128662
[43,] -12.394920 -12234.600024
[44,] 2685.480561 -12.394920
[45,] 1845.478000 2685.480561
[46,] 5753.176207 1845.478000
[47,] 4199.933590 5753.176207
[48,] 2978.208801 4199.933590
[49,] -906.891190 2978.208801
[50,] -1558.890906 -906.891190
[51,] 2612.794539 -1558.890906
[52,] 3975.140120 2612.794539
[53,] 2280.686201 3975.140120
[54,] -5153.210752 2280.686201
[55,] -6467.591056 -5153.210752
[56,] -2417.173620 -6467.591056
[57,] -3781.377900 -2417.173620
[58,] 8178.751641 -3781.377900
[59,] 6821.989022 8178.751641
[60,] 4698.867316 6821.989022
[61,] 2175.250568 4698.867316
[62,] 5794.431819 2175.250568
[63,] 2125.648027 5794.431819
[64,] -3304.045650 2125.648027
[65,] -12167.135708 -3304.045650
[66,] -4220.333381 -12167.135708
[67,] -6129.006728 -4220.333381
[68,] -6188.204132 -6129.006728
[69,] 796.417358 -6188.204132
[70,] 5719.420059 796.417358
[71,] -1644.655268 5719.420059
[72,] 3200.763251 -1644.655268
[73,] -6559.795807 3200.763251
[74,] -2450.050389 -6559.795807
[75,] -182.870961 -2450.050389
[76,] 335.806926 -182.870961
[77,] -3093.606115 335.806926
[78,] -16044.544229 -3093.606115
[79,] -14707.376232 -16044.544229
[80,] -17555.307240 -14707.376232
[81,] -5683.991842 -17555.307240
[82,] -4363.024873 -5683.991842
[83,] -2031.834796 -4363.024873
[84,] 1362.584831 -2031.834796
[85,] -2593.097300 1362.584831
[86,] -2251.631204 -2593.097300
[87,] -7544.811623 -2251.631204
[88,] -9557.844865 -7544.811623
[89,] -7423.926001 -9557.844865
[90,] -6563.806247 -7423.926001
[91,] -4182.478276 -6563.806247
[92,] -437.431360 -4182.478276
[93,] -852.140650 -437.431360
[94,] 804.140640 -852.140650
[95,] -497.274824 804.140640
[96,] -7518.915034 -497.274824
[97,] -5599.862508 -7518.915034
[98,] -5123.199055 -5599.862508
[99,] -2823.927161 -5123.199055
[100,] 574.867319 -2823.927161
[101,] -3471.456851 574.867319
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3967.077488 16109.527797
2 1878.412702 3967.077488
3 -1843.181046 1878.412702
4 -278.047646 -1843.181046
5 -3.629429 -278.047646
6 -9859.058372 -3.629429
7 -3484.340160 -9859.058372
8 5363.935088 -3484.340160
9 -889.105089 5363.935088
10 3115.835061 -889.105089
11 2562.800843 3115.835061
12 -1755.378415 2562.800843
13 2198.568052 -1755.378415
14 2284.528755 2198.568052
15 4699.237074 2284.528755
16 4318.231227 4699.237074
17 2387.912039 4318.231227
18 11053.066845 2387.912039
19 4341.776479 11053.066845
20 1430.708655 4341.776479
21 14592.098413 1430.708655
22 5285.501573 14592.098413
23 1789.894904 5285.501573
24 2912.549231 1789.894904
25 3776.484679 2912.549231
26 8322.333218 3776.484679
27 2213.489479 8322.333218
28 3906.646773 2213.489479
29 -1629.994067 3906.646773
30 -1773.597195 -1629.994067
31 -2680.787134 -1773.597195
32 3524.415286 -2680.787134
33 5911.497794 3524.415286
34 5878.524229 5911.497794
35 3499.266088 5878.524229
36 3387.465848 3499.266088
37 5990.867156 3387.465848
38 9320.650922 5990.867156
39 8597.619187 9320.650922
40 6478.204214 8597.619187
41 -522.128662 6478.204214
42 -12234.600024 -522.128662
43 -12.394920 -12234.600024
44 2685.480561 -12.394920
45 1845.478000 2685.480561
46 5753.176207 1845.478000
47 4199.933590 5753.176207
48 2978.208801 4199.933590
49 -906.891190 2978.208801
50 -1558.890906 -906.891190
51 2612.794539 -1558.890906
52 3975.140120 2612.794539
53 2280.686201 3975.140120
54 -5153.210752 2280.686201
55 -6467.591056 -5153.210752
56 -2417.173620 -6467.591056
57 -3781.377900 -2417.173620
58 8178.751641 -3781.377900
59 6821.989022 8178.751641
60 4698.867316 6821.989022
61 2175.250568 4698.867316
62 5794.431819 2175.250568
63 2125.648027 5794.431819
64 -3304.045650 2125.648027
65 -12167.135708 -3304.045650
66 -4220.333381 -12167.135708
67 -6129.006728 -4220.333381
68 -6188.204132 -6129.006728
69 796.417358 -6188.204132
70 5719.420059 796.417358
71 -1644.655268 5719.420059
72 3200.763251 -1644.655268
73 -6559.795807 3200.763251
74 -2450.050389 -6559.795807
75 -182.870961 -2450.050389
76 335.806926 -182.870961
77 -3093.606115 335.806926
78 -16044.544229 -3093.606115
79 -14707.376232 -16044.544229
80 -17555.307240 -14707.376232
81 -5683.991842 -17555.307240
82 -4363.024873 -5683.991842
83 -2031.834796 -4363.024873
84 1362.584831 -2031.834796
85 -2593.097300 1362.584831
86 -2251.631204 -2593.097300
87 -7544.811623 -2251.631204
88 -9557.844865 -7544.811623
89 -7423.926001 -9557.844865
90 -6563.806247 -7423.926001
91 -4182.478276 -6563.806247
92 -437.431360 -4182.478276
93 -852.140650 -437.431360
94 804.140640 -852.140650
95 -497.274824 804.140640
96 -7518.915034 -497.274824
97 -5599.862508 -7518.915034
98 -5123.199055 -5599.862508
99 -2823.927161 -5123.199055
100 574.867319 -2823.927161
101 -3471.456851 574.867319
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/73bww1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ekdz1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ekdz1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ekdz1290241573.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11z2bn1290241573.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12llss1290241573.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13a4pm1290241573.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/142d6p1290241573.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/156wnd1290241573.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/162olm1290241573.tab")
+ }
>
> try(system("convert tmp/1iaxq1290241573.ps tmp/1iaxq1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/2iaxq1290241573.ps tmp/2iaxq1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/3iaxq1290241573.ps tmp/3iaxq1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bkxt1290241573.ps tmp/4bkxt1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bkxt1290241573.ps tmp/5bkxt1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bkxt1290241573.ps tmp/6bkxt1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/73bww1290241573.ps tmp/73bww1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ekdz1290241573.ps tmp/8ekdz1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ekdz1290241573.ps tmp/9ekdz1290241573.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ekdz1290241573.ps tmp/10ekdz1290241573.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.005 1.621 6.706